Business organizations and educational institutions need data to be accessed by multiple individuals concurrently. One of the methods of achieving concurrent data access by storing the data on servers. Servers can generally be defined as computers with large computing power and memory, which can service multiple clients at the same time. With an increase in number of concurrent accesses, the number of servers for storing data increases. This phenomenon gives rise to a problem called ‘server sprawling’.
Server sprawling is common in data centers of business organizations. Server sprawls are characterized by the use of dedicated servers for single applications. This leads to situations where the business organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources than are justified by their present workloads. As business organizations invest substantial amounts of money in these data centers, most business organizations are looking towards server consolidation for trimming unnecessary costs and maximizing their returns on investment. Consolidating multiple under-utilized servers into small number of servers is an effective tool for businesses to enhance their return on investment.
Consolidation helps eliminate Information Technology (IT) redundancies, achieve increased asset utilization, reduce operational, and maintenance costs. However, consolidation is often done manually after analyzing the historical workload pattern of the servers. One method existing in the art describes a high dimensional probabilistic bin packing model for the server consolidation problem. Another method in the art describes heuristic techniques for improving the Least Loaded (LL) and the First Fit Decreasing (FFD) algorithms for reducing the number of destination servers. However, the existing methods in the art involve a lot of manual effort. The manual efforts involved include the analysis of historical workload patterns. Manual efforts are time consuming, error prone, and are dependent on the subjective assessment of the decision maker.
Consequently, there is a need for a method and a system for consolidation of servers without manual labor. Additionally, there is a need for a method and system that consolidates the servers with an accuracy that is not possible with manual interference.